Please take a look at part 1 for a short introduction to AI in general.
Many Ways To Apply AI To Education, (Part 2)
Usually when people talk about AI they also include doing something with the patterns AI has found. For example, an AI based self-driving car wouldn’t be very useful if the system didn’t also control the motion of the car based on the data from from the car’s sensors.
When we are talking about AI in education it all comes down to having lots of data from each student’s activities and learning process. With this data we can use an AI to build individual learner profiles of the students. This is very similar to how a teacher is able to understand their students by observing them over time. The difference is that an AI can observe all details from every student all the time and detect changes in students’ behavior and success very quickly.
There are
many
ways we can improve both teaching and learning using
this approach. For example, we can improve the personal guidance the teacher gives to a student with detailed
learning analytics and by pointing out opportunities for positive feedback.
AI Based Adaptive Learning
However, the most interesting opportunity is to take differentiation to the next level. With AI we can observe students’ learning in real time and tweak the learning contents for each student based on their current performance. If we calibrate the contents and the difficulty level for each student we can improve everyone’s motivation and engagement a lot.
We can also help the students progress faster by continuously adjusting individual learning paths. After all, the AI does not have to wait until the next test to determine if they know a subject well enough to tackle the next one.
What Do We Need For AI Based Adaptive Learning?
To build such a system we need two things.
First, the AI must be able to observe students’ actions in great detail and frequency. Infrequent measurements like test results or subjective data like evaluations from teacher, peers or students themselves is not nearly enough. We need full access to the entire learning process: what content each student looks at, what exercises they do, when they do them and so forth. Only then can an AI draw robust conclusions about the student’s strengths and weaknesses.
Second, we need learning paths that can be controlled by a computer. The learning paths should offer their contents and exercises in small portions so that the AI engine can keep the feedback loop as short.
Teachers Are More Important Than Ever
The technologies are there today to build this kind of fully automated, adaptive AI learning engines. However,
like any technology AI is not infallible, and teachers remain the best experts in their classrooms. Tools like
this bring the most benefits when provided as helpers to the teacher in charge of their students’ learning
process.
